Book description
Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow.
About This Book
 Understand predictive analytics along with its challenges and best practices
 Embedded with assessments that will help you revise the concepts you have learned in this book
Who This Book Is For
This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.
What You Will Learn
 Learn TensorFlow features in a reallife problem, followed by detailed TensorFlow installation and configuration
 Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
 Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
 Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
 Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on reallife datasets
In Detail
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis.
This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for highdimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using KmeansYou will then learn how to predict neighborhoods using Kmeans, and then, see another example of clustering audio clips based on their audio features.
This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.
This book is embedded with useful assessments that will help you revise the concepts you have learned in this book.
Style and approach
This is a fastpaced guide that provides a quick learning solution to all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow
Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product:
 Predictive Analytics with TensorFlow by Md. Rezaul Karim
Publisher resources
Table of contents

TensorFlow: Powerful Predictive Analytics with TensorFlow
 TensorFlow: Powerful Predictive Analytics with TensorFlow
 Credits
 Preface

1. From Data to Decisions – Getting Started with TensorFlow
 Taking Decisions Based on Data – Titanic Example
 General Overview of TensorFlow
 Installing and Configuring TensorFlow
 TensorFlow Computational Graph
 TensorFlow Programming Model
 Data Model in TensorFlow
 TensorBoard
 Getting Started with TensorFlow – Linear Regression and Beyond
 Summary
 Assessments
 2. Putting Data in Place – Supervised Learning for Predictive Analytics
 3. Clustering Your Data – Unsupervised Learning for Predictive Analytics
 4. Using Reinforcement Learning for Predictive Analytics
 A. Assessment Answers
Product information
 Title: TensorFlow: Powerful Predictive Analytics with TensorFlow
 Author(s):
 Release date: March 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789136913
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